Information processing device, information processing method, and program

ABSTRACT

Provided is an information processing device including a collection unit configured to collect meta feedback that is feedback on a combination including a content, a user, and feedback on the content of the user, and a prediction unit configured to obtain a predicted acceptance degree that is a prediction value of a degree to which an active user that is a target user accepts a combination including at least one of the content, the user, and the feedback, based on the collected meta feedback.

CROSS REFERENCE TO PRIOR APPLICATION

This application is a continuation of U.S. patent application Ser. No.13/559,099 (filed on Jul. 26, 2012), which claims priority to JapanesePatent Application No. 2011-168975 (filed on Aug. 2, 2011), which areall hereby incorporated by reference in their entirety.

BACKGROUND

The present disclosure relates to an information processing device, aninformation processing method, and a program, and more particularly, toan information processing device, an information processing method, anda program that are suitable to be employed at the time of recommendingcontent or the like.

In the technical field in which content or the like is recommended,various plans for allowing the recommended content or the like to beaccepted by a user are typically devised.

For example, with collaborative filtering or the like heading the list,techniques of improving prediction accuracy of evaluation on the contentby a user and recommending the content matching the preference of theuser are employed.

In addition, for example, a technique of extracting keywords or the likeindicating features of the recommended content and presenting thekeywords or the like as a recommendation reason has been proposed (e.g.,see Japanese Laid-Open Patent Publication No. 2007-58842).

SUMMARY

The recommended content or the like is thus expected to be reliablyaccepted by the user in the technical field in which the content or thelike is recommended.

The present disclosure is made to increase the possibility of therecommended content or the like being accepted by the user.

According to a first aspect of the present disclosure, there is providedan information processing device which includes: a collection unitconfigured to collect meta feedback that is feedback on a combinationincluding a content, a user, and feedback on the content of the user;and a prediction unit configured to obtain a predicted acceptance degreethat is a prediction value of a degree to which an active user that is atarget user accepts a combination including at least one of the content,the user, and the feedback, based on the collected meta feedback.

The information processing device may further include: a selection unitconfigured to select a combination including a content to be presentedto the active user and at least one of the user that has given feedbackon the content and the feedback, based on the predicted acceptancedegree; and a presentation control unit configured to control thecontent included in the selected combination and at least one of theuser and the feedback included in the selected combination to bepresented to the active user.

The selection unit may preferentially select a combination having ahigher predicted acceptance degree.

The information processing device may further include: a selection unitconfigured to select a combination of the content and the user thatprompts provision of feedback based on the predicted acceptance degreeof the active user with respect to the combination of the content andthe user or the predicted acceptance degree of the active user withrespect to the user; a guidance unit configured to prompt the userincluded in the selected combination to give feedback on the contentincluded in the selected combination; and a presentation control unitconfigured to control the content included in the selected combinationand at least one of the user included in the selected combination andthe feedback given by the user to be presented to the active user.

The selection unit may preferentially select a combination of thecontent and the user having a higher predicted acceptance degree or acombination of the content and the user including the user having thehigher predicted acceptance degree.

The information processing device may further include: a counting unitconfigured to count the predicted acceptance degrees of a plurality ofactive users for each user, for each feedback, or for each combinationof the user and the feedback; a selection unit configured to select acombination including a content to be presented and at least one of theuser and the feedback, based on the counted result of the predictedacceptance degrees; and a presentation control unit configured tocontrol the content included in the selected combination and at leastone of the user and the feedback included in the selected combination tobe presented.

The selection unit may preferentially select the combination includingthe user and the feedback having higher predicted acceptance degrees ofthe plurality of active users.

The information processing device may further include: a counting unitconfigured to count the predicted acceptance degrees of a plurality ofactive users with respect to a combination of the content and the useror the predicted acceptance degrees of the plurality of active userswith respect to the user for each user; a selection unit configured toselect a combination of the content and the user that prompts provisionof feedback, based on the counted result of the predicted acceptancedegrees; a guidance unit configured to prompt the user included in theselected combination to give feedback on the content included in theselected combination; and a presentation control unit configured tocontrol the content included in the selected combination and at leastone of the user included in the selected combination and the feedbackgiven by the user to be presented.

The selection unit may preferentially select a combination including theuser having higher predicted acceptance degrees of the plurality ofactive users.

The information processing device may further include: a counting unitconfigured to count the predicted acceptance degree of the active userwith respect to the combination including at least the user for eachuser; a selection unit configured to preferentially select the userhaving a higher predicted acceptance degree of the active user; and apresentation control unit configured to control the selected user to bepresented to the active user.

The prediction unit may include: an acceptance model generation unitconfigured to generate an acceptance model for obtaining the predictedacceptance degree, based on the collected meta feedback; and anacceptance prediction unit configured to obtain the predicted acceptancedegree based on the acceptance model.

According to the first aspect of the present disclosure, there isprovided an information processing method which includes: by aninformation processing device, collecting meta feedback that is feedbackon a combination including a content, a user, and feedback on thecontent of the user; and by the information processing device, obtaininga predicted acceptance degree that is a prediction value of a degree towhich an active user that is a target user accepts a combinationincluding at least one of the content, the user, and the feedback, basedon the collected meta feedback.

According to the first aspect of the present disclosure, there isprovided a program for causing a computer to execute processesincluding: collecting meta feedback that is feedback on a combinationincluding a content, a user, and feedback on the content of the user;and obtaining a predicted acceptance degree that is a prediction valueof a degree to which an active user that is a target user accepts acombination including at least one of the content, the user, and thefeedback, based on the collected meta feedback.

According to a second aspect of the present disclosure, there isprovided an information processing device which includes: a collectionunit configured to collect meta feedback that is feedback on acombination including a content and feedback on the content; and aprediction unit configured to obtain a predicted acceptance degree thatis a prediction value of a degree to which an active user that is atarget user accepts a combination including at least one of the contentand the feedback, based on the collected meta feedback.

The information processing device may further include: a selection unitconfigured to select a combination including a content to be presentedto the active user and the feedback given on the content, based on thepredicted acceptance degree; and a presentation control unit configuredto control the content included in the selected combination and thefeedback included in the selected combination to be presented to theactive user.

The information processing device may further include: a counting unitconfigured to count the predicted acceptance degrees of a plurality ofactive users for each feedback; a selection unit configured to select acombination including the content to be presented and the feedback,based on the counted result of the predicted acceptance degrees; and apresentation control unit configured to control the content included inthe selected combination and the feedback included in the selectedcombination to be presented.

The prediction unit may include: an acceptance model generation unitconfigured to generate an acceptance model for obtaining the predictedacceptance degree, based on the collected meta feedback; and anacceptance prediction unit configured to obtain the predicted acceptancedegree based on the acceptance model.

According to the second aspect of the present disclosure, there isprovided an information processing method which includes: by aninformation processing device, collecting meta feedback that is feedbackon a combination including a content and feedback on the content; and bythe information processing device, obtaining a predicted acceptancedegree that is a prediction value of a degree to which an active userthat is a target user accepts a combination including at least one ofthe content and the feedback, based on the collected meta feedback.

According to the second aspect of the present disclosure, there isprovided a program for causing a computer to execute processesincluding: collecting meta feedback that is feedback on a combinationincluding a content and feedback on the content; and obtaining apredicted acceptance degree that is a prediction value of a degree towhich an active user that is a target user accepts a combinationincluding at least one of the content and the feedback, based on thecollected meta feedback.

According to the first aspect of the present disclosure, meta feedbackthat is feedback on a combination including a content, a user, andfeedback on the content of the user is collected, and a predictedacceptance degree that is a prediction value of a degree to which anactive user that is a target user accepts a combination including atleast one of the content, the user, and the feedback is obtained basedon the collected meta feedback.

According to the second aspect of the present disclosure, meta feedbackthat is feedback on a combination including a content and feedback onthe content is collected, and a predicted acceptance degree that is aprediction value of a degree to which an active user that is a targetuser accepts a combination including at least one of the content and thefeedback is obtained based on the collected meta feedback.

According to the first aspect or the second aspect of the presentdisclosure, the possibility of the recommended content or the like beingaccepted by the user can be increased.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an embodiment of an informationprocessing system to which the present disclosure is applied;

FIG. 2 is a diagram illustrating an example of data configuration of auser network DB;

FIG. 3 is a flowchart illustrating an acceptance model generationprocess;

FIG. 4 is a diagram illustrating a first example of a contentpresentation screen;

FIG. 5 is a diagram illustrating a second example of a contentpresentation screen;

FIG. 6 is a diagram illustrating an example of data configuration of afeedback DB;

FIG. 7 is a diagram illustrating an example of data configuration of ameta feedback DB;

FIG. 8 is a diagram illustrating an example of data configuration of afeedback DB after feedback analysis;

FIG. 9 is a diagram illustrating an example of data configuration of ameta feedback DB after feedback analysis;

FIG. 10 is a diagram illustrating an example of data configuration of ameta feedback DB after feedback discrimination;

FIG. 11 is a diagram illustrating a specific example of data of metafeedback used to generate an acceptance model with collaborativefiltering;

FIG. 12 is a diagram illustrating a specific example of data of metafeedback used to generate an acceptance model with collaborativefiltering;

FIG. 13 is a diagram illustrating a specific example of data of metafeedback used to generate an acceptance model with collaborativefiltering;

FIG. 14 is a diagram illustrating an example of a latent vector withrespect to an active user included in an acceptance model usingcollaborative filtering;

FIG. 15 is a diagram illustrating an example of a latent vector withrespect to content included in an acceptance model using collaborativefiltering;

FIG. 16 is a diagram illustrating an example of a latent vector withrespect to a user included in an acceptance model using collaborativefiltering;

FIG. 17 is a diagram illustrating an example of a weight vector includedin an acceptance model using collaborative filtering;

FIG. 18 is a diagram illustrating an example of feature quantities of aCUF tuple;

FIG. 19 is a diagram illustrating an example of weights with respect torespective feature quantities used for the acceptance model withcontent-based filtering (CBF);

FIG. 20 is a flowchart illustrating a first embodiment of a contentrecommendation process;

FIG. 21 is a diagram illustrating a first example of acceptanceprediction results;

FIG. 22 is a diagram illustrating a first example of a contentrecommendation screen;

FIG. 23 is a diagram illustrating a second example of a contentrecommendation screen;

FIG. 24 is a diagram illustrating a third example of a contentrecommendation screen;

FIG. 25 is a diagram illustrating a fourth example of a contentrecommendation screen;

FIG. 26 is a diagram illustrating a fifth example of a contentrecommendation screen;

FIG. 27 is a diagram illustrating a sixth example of a contentrecommendation screen;

FIG. 28 is a flowchart illustrating a second embodiment of a contentrecommendation process;

FIG. 29 is a flowchart illustrating a third embodiment of a contentrecommendation process;

FIG. 30 is a diagram illustrating a second example of acceptanceprediction results;

FIG. 31 is a diagram illustrating the acceptance prediction results ofFIG. 30 sorted by content ID and user ID;

FIG. 32 is a flowchart illustrating a fourth embodiment of a contentrecommendation process;

FIG. 33 is a flowchart illustrating a user recommendation process; and

FIG. 34 is a block diagram illustrating a configuration example of acomputer.

DETAILED DESCRIPTION OF THE EMBODIMENT(S)

Hereinafter, preferred embodiments of the present disclosure will bedescribed in detail with reference to the appended drawings. Note that,in this specification and the appended drawings, structural elementsthat have substantially the same function and structure are denoted withthe same reference numerals, and repeated explanation of thesestructural elements is omitted.

Hereinafter, forms for embodying the present disclosure (which will bereferred to as embodiments) will be described in the following order:

1. Embodiments

1-1. Configuration Example of Information Processing System 1

1-2. Process of Information Processing System 1

2. Modified Examples

In addition, hereinafter, terms used in the present specification aredefined as follows.

An active user indicates a target user to whom the content or the likeis to be recommended.

Feedback is a user response to the presented content or the like, and islargely classified into explicit feedback and implicit feedback. Theexplicit feedback includes, for example, likes/dislikes, evaluationinformation such as 5-phase evaluation, a comment using tags of wordlevel or natural sentences, and icons. The implicit feedback includes,for example, operation on a user terminal such as reproduction, stop,and skip, content purchase, and so forth.

In addition, hereinafter, feedback on a combination of a presentedcontent, a user, and feedback on the content of the user, or feedback ona combination of the presented content and the feedback on the contentis particularly referred to as meta feedback.

A CUF tuple is a combination set of three elements including thecontent, the user, and the feedback on the content of the user. Thetuple is also employed for the set including the content, the user, anda part of the feedback in a similar way. For example, the set includingthe content only is referred to as a C tuple, and the set including thecontent and the user is referred to as a CU tuple. That is, the tuplehas a plurality of types in accordance with kinds of items to beincluded.

In addition, hereinafter, for example, a tuple including a content Ci (iis a natural number), a user Uj (j is a natural number), and feedback Fk(k is a natural number) is represented as a (Ci, Uj, Fk) tuple.

1. Embodiments 1-1. Configuration Example of Information ProcessingSystem 1

FIG. 1 is a block diagram illustrating an embodiment of an informationprocessing system 1 to which the present disclosure is applied.

The information processing system 1 includes a server 11 and userterminals 12-1 to 12-n. The server 11 and the user terminals 12-1 to12-n are interconnected via a network 13.

In addition, hereinafter, when it is not necessary to individuallydistinguish between the user terminals 12-1 to 12-n, the user terminalsare simply referred to as the user terminal 12.

The server 11 provides each user terminal 12 with the content or thelike or recommends the content or the like to each user terminal.

In addition, the kind of the content is not particularly limited, andvarious contents such as moving images such as movies or televisionprograms, still images such as photos or pictures, music data,electronic books, games, or document files, for example, may beemployed.

The server 11 includes a communication unit 21, a feedback collectionunit 22, a feedback database (DB) 23, a meta feedback DB 24, a feedbackanalysis unit 25, a feedback discrimination unit 26, a user DB 27, acontent metadata DB 28, a prediction unit 29, a prediction counting unit30 a content promotion DB 31, a user network DB 32, a presentation tupleselection unit 33, a presentation control unit 34, and a feedbackguidance unit 35.

The communication unit 21 communicates with each user terminal 12 andtransceives data via the network 13.

The feedback collection unit 22 collects feedback information byreceiving feedback given by the active user or feedback informationassociated with meta feedback from each user terminal 12 via the network13 and the communication unit 21. The feedback collection unit 22 storesthe feedback information in the feedback DB 23 when the collectedfeedback information is associated with the feedback. On the other hand,the feedback collection unit 22 stores the feedback information in themeta feedback DB 24 when the received feedback information is associatedwith the meta feedback on the CUF tuple.

The feedback DB 23 stores information associated with the feedback givenon each content by the active user. In addition, data configuration ofthe feedback DB 23 will be described later with reference to FIG. 6 orthe like.

The meta feedback DB 24 stores information associated with the metafeedback given on each CUF tuple by the active user. In addition, dataconfiguration of the meta feedback DB 24 will be described later withreference to FIG. 7 or the like.

The feedback analysis unit 25 analyzes the feedback stored in thefeedback DB 23, and stores the analysis result in the feedback DB 23. Inaddition, the feedback analysis unit 25 analyzes the meta feedbackstored in the meta feedback DB 24, and stores the analysis result in themeta feedback DB 24.

The feedback discrimination unit 26 discriminates whether the metafeedback stored in the meta feedback DB 24 is positive or negative, andstores the discrimination result in the meta feedback DB 24.

The user DB 27 stores information (e.g., feature quantities such as age,sex, job, residence, or the like) associated with the user that uses theinformation processing system 1.

The content metadata DB 28 stores metadata (e.g., feature quantitiessuch as genres or keywords of the content, content names, imagesindicating the contents, and so forth) associated with the contentprovided by the server 11.

The prediction unit 29 predicts the degree to which each active useraccepts a tuple including at least one of the content, the user, and thefeedback based on the meta feedback collected from each user terminal12, and obtains a predicted acceptance degree as the prediction value.

Here, the behavior of the active user accepting the CUF tuple includes,for example, internal behavior such as having a positive feeling aboutthe presented CUF tuple and external behavior such as taking positiveaction about the presented CUF tuple. For example, the former mayinclude having a favorable impression of or interest in the presentedCUF tuple. For example, the latter may include giving positive feedbackon the presented CUF tuple, purchasing the content included in the CUFtuple, using the tuple (reproduction, download, and so forth),recommending the tuple to another user, checking information associatedwith the corresponding content, and so forth.

In addition, the same goes for acceptance of tuples of types other thanthe CUF tuple, contents, users, and so forth.

The prediction unit 29 includes an acceptance model generation unit 51,an acceptance model DB 52, and an acceptance prediction unit 53.

The acceptance model generation unit 51 generates an acceptance modelfor obtaining the predicted acceptance degree of each active user withrespect to the tuple of each type based on the meta feedback collectedfrom each user terminal 12. That is, as will be described later, theacceptance model generation unit 51 uses the information stored in thefeedback DB 23, the meta feedback DB 24, the user DB 27, and the contentmetadata DB 28 to generate the acceptance model with respect to thetuple of each type. The acceptance model generation unit 51 stores thegenerated acceptance model in the acceptance model DB 52.

The acceptance prediction unit 53 predicts the degree to which eachactive user accepts the tuple of each type based on the acceptance modelstored in the acceptance model DB 52, and obtains the predictedacceptance degree. That is, as will be described later, the acceptanceprediction unit 53 uses the information stored in the feedback DB 23,the user DB 27, the content metadata DB 28, and the acceptance model DB52 to obtain the predicted acceptance degree of each active user withrespect to the tuple of each type. The acceptance prediction unit 53notifies the prediction counting unit 30 and the presentation tupleselection unit 33 of the prediction result.

The prediction counting unit 30 carries out various counts of theprediction results from the acceptance prediction unit 53, and notifiesthe presentation tuple selection unit 33 of the counted results.

In the content promotion DB 31, (ID of) the content on which a serviceprovider of providing the service using the information processingsystem 1 carries out promotion such as sales promotion is registered.The content of which the content ID is registered in the contentpromotion DB 31 is thus preferentially recommended to the user, forexample.

The user network DB 32 is a DB in which information indicating arelation between the users using the information processing system 1 isstored. FIG. 2 illustrates an example of data configuration of the usernetwork DB 32. The user network DB 32 includes items for a relation ID,a From ID, and a To ID.

The relation ID is an ID for identifying each relation between users.

The From ID is a user ID of the user of the follow-source.

The To ID is a user ID of the user of the follow-destination.

It is thus shown that the user U1 follows the user U2 in the record 1 ofthe relation ID, for example. That is, it is shown that the user U1knows the user U2 or has an interest in the user U2. In addition, it isnot necessary for the following to be bidirectional, and the followingmay also be only unidirectional.

The presentation tuple selection unit 33 selects the presentation tuplethat is a combination including at least one of the content, the user,and the feedback to be presented to the active user based on theprediction result from the acceptance prediction unit 53, the countedresult from the prediction counting unit 30, and the information storedin the content promotion DB 31 and the user network DB 32. Thepresentation tuple selection unit 33 notifies the presentation controlunit 34 and the feedback guidance unit 35 of the selected presentationtuple.

The presentation control unit 34 generates display data for displayingthe screen that presents the content or the like based on thepresentation tuple selected by the presentation tuple selection unit 33and the information stored in the feedback DB 23, the user DB 27, thecontent metadata DB 28, and the user network DB 32. The presentationcontrol unit 34 transmits the generated display data to the userterminal 12 via the communication unit 21 and the network 13.

The feedback guidance unit 35 generates display data for screen displayprompting the user to give feedback on a specific content based on thepresentation tuple selected by the presentation tuple selection unit 33and the information stored in the user DB 27 and the content metadata DB28. The feedback guidance unit 35 transmits the generated display datato the user terminal 12 via the communication unit 21 and the network13.

The user terminal 12 is also used to utilize the content presented fromthe server 11 or to display a variety of information, for example. Inaddition, the active user may use an operation unit (not shown) of theuser terminal 12 to give feedback on the content or meta feedback on theCUF tuple.

In addition, for example, the user terminal 12 includes a computersystem such as a personal computer, a computer game system or a homeserver, or a portable computer system such as a portable game machine, acellular phone, a personal digital assistant or a portable music player.

1-2. Process of Information Processing System 1

Next, the process carried out by the information processing system 1will be described with reference to FIG. 3 to FIG. 33.

(Feedback Collection Process)

First, an acceptance model generation process carried out by theinformation processing system 1 will be described with reference to theflowchart of FIG. 3.

In step S1, the server 11 presents the content. In particular, forexample, the user terminal 12 transmits an instruction to present thecontent input by the active user to the server 11 via the network 13.

The communication unit 21 of the server 11 receives the instruction fromthe user terminal 12 and supplies the instruction to the presentationcontrol unit 34. The presentation control unit 34 acquires information(metadata) associated with the content to be presented to the activeuser from the content metadata DB 28. In addition, the presentationcontrol unit 34 acquires information associated with feedback on thecontent to be presented from the feedback DB 23. In addition, thepresentation control unit 34 acquires information associated with theuser that has given the feedback on the content to be presented from theuser DB 27.

The presentation control unit 34 generates display data for displayingthe content presentation screen for presenting the content to the activeuser based on the acquired information. The presentation control unit 34then transmits the generated display data to the user terminal 12 of theactive user via the communication unit 21 and the network 13.

The user terminal 12 displays the content presentation screen based onthe display data received from the server 11.

FIGS. 4 and 5 illustrate examples of the content presentation screen.

FIG. 4 illustrates an example displaying the information associated withthe content along with feedback information such as a comment or thelike given by the user. In particular, a content name (content A) isdisplayed in an upper left corner, and an image 101 representing thecontent and a user evaluation 102 for the corresponding content aredisplayed below the content name. For example, the user evaluation 102is represented by an average value of 5-phase evaluations given by aplurality of users. In addition, a button 103 is displayed below theuser evaluation 102. A comment input screen is displayed when the button103 is pressed, and the active user may input the comment as feedback onthe content A.

In addition, information associated with the feedback on the content Ais displayed in the right side of the display described above. That is,an image 104 a representing the user A that has given the feedback onthe content A, a user name of the user A (including a nickname or thelike), and a balloon 105 a including contents (e.g., a comment or thelike) of the feedback given by the user A are displayed. Similarly, animage 104 b representing the user B that has given the feedback on thecontent A, a user name of the user B, and a balloon 105 b includingcontents of the feedback given by the user B are displayed.

In addition, a thumbs-up button 106 a and a thumbs-down button 107 a aredisplayed to the right of the balloon 104 a. The active user presses thethumbs-up button 106 a when the active user gives a positive evaluation(e.g., agreement, sympathy, or the like) for the feedback on the contentA of the user A. On the other hand, the active user presses thethumbs-down button 107 a when the active user gives a negativeevaluation (e.g., disagreement, displeasure, or the like) for thefeedback on the content A of the user A.

Similarly, a thumbs-up button 106 b and a thumbs-down button 107 b forgiving an evaluation on the feedback on the content A of the user B arealso displayed to the right of the balloon 104 b.

Accordingly, in the content presentation screen of FIG. 4, the activeuser can directly give the feedback on the presented content byinputting the comment. In addition, the active user can give explicitmeta feedback on the set (CUF tuple) of the presented content, the userthat has given the feedback, and the given feedback by pressing thethumbs-up buttons 106 a and 106 b and the thumbs-down buttons 107 a and107 b.

FIG. 5 illustrates an example displaying feedback information of eachuser in a list form. In this case, the feedback information on thecontent A of the user A is displayed in the first line, and the feedbackinformation on the content B of the user B is displayed in the secondline.

In particular, an image 121 a representing the content A, a content name(content A), a user evaluation 122 a on the content A, an image 123 arepresenting the user A, and a balloon 124 a including the feedback onthe content A of the user A are displayed in the first line.

In addition, a view button 125 a and a purchase button 126 a aredisplayed to the right of the balloon 124 a. The active user can viewthe content A by pressing the view button 125 a and can purchase thecontent A by pressing the purchase button 126 a.

Similarly, an image 121 b representing the content B, a content name(content B), user evaluation 122 b on the content B, an image 123 brepresenting the user B, and a balloon 124 b including the feedback onthe content B of the user B are displayed in the second line. Inaddition, a view button 125 b and a purchase button 126 b are displayedto the right of the balloon 124 b.

Accordingly, in the content presentation screen of FIG. 5, the activeuser can give implicit meta feedback on the set (CUF tuple) of thepresented content, the user that has given the feedback on the content,and the given feedback by pressing the view buttons 125 a and 125 b andthe purchase buttons 126 a and 126 b.

In addition, hereinafter, the thumbs-up buttons are simply referred toas a thumbs-up button 106 when it is not necessary to individuallydistinguish between the thumbs-up buttons 106 a and 106 b, and thethumbs-down buttons are simply referred to as a thumbs-down button 107when it is not necessary to individually distinguish between thethumbs-up buttons 107 a and 107 b. In addition, hereinafter, the viewbuttons are simply referred to as a view button 125 when it is notnecessary to individually distinguish between the view buttons 125 a and125 b, and the purchase buttons are simply referred to as a purchasebutton 126 when it is not necessary to individually distinguish betweenthe purchase buttons 126 a and 126 b.

In step S2, the server 11 acquires feedback. In particular, for example,the active user directly gives the feedback on the presented content byinputting the 5-phase evaluations or the comment on the contentpresented in the content presentation screen using a predeterminedinterface. Alternatively, for example, the active user gives metafeedback on the presented CUF tuple by pressing the thumbs-up button 106and the thumbs-down button 107 of FIG. 4 or the view button 125 and thepurchase button 126 of FIG. 5.

The user terminal 12 transmits the feedback given by the active user orthe feedback information associated with the meta feedback to the server11 via the network 13.

The communication unit 21 of the server 11 receives the feedbackinformation transmitted from the user terminal 12, and supplies thefeedback information to the feedback collection unit 22. The feedbackcollection unit 22 stores the feedback information in the feedback DB 23when the received feedback information is associated with the feedbackon the presented content. On the other hand, the feedback collectionunit 22 stores the feedback information in the meta feedback DB 24 whenthe received feedback information is associated with the meta feedbackon the presented CUF tuple.

FIG. 6 illustrates an example of data configuration of the feedback DB23. In this case, the feedback DB 23 includes items such as a feedbackID, a user ID, a content ID, a feedback type, and feedback.

The feedback ID is an ID for identifying individual feedback.

The user ID is a user ID for allowing the user to give the feedback.

The content ID is a content ID of the content on which the feedback isgiven.

The feedback type indicates the type of the given feedback, and isclassified into “character string,” “5-phase,” “operation,” and soforth, for example. For example, the feedback using characterinformation such as comment or tag is classified into “characterstring.” The feedback using the 5-phase evaluation is classified into“5-phase.” The feedback using user operations such as reproduction orskip with respect to the content is classified into “operation.”

The feedback item indicates contents of the feedback actually given bythe user.

For example, information indicating that the user U2 has given thefeedback of the character string type on the content C1 such as “This iscool!” is stored in the record F1 of the feedback ID of FIG. 6.Information indicating that the user U3 has given the evaluation of “5”of the 5-phase evaluation on the content C3 is stored in the record F2of the feedback ID. Information indicating that the user U3 has carriedout the “reproduction” operation on the content C3 is stored in therecord F3 of the feedback ID. Information indicating that the user U1has given the evaluation “2” of the 5-phase evaluation on the content C2is stored in the record F4 of the feedback ID.

FIG. 7 illustrates an example of data configuration of the meta feedbackDB 24. In this case, the meta feedback DB 24 includes items for a metafeedback ID, a user ID, a target feedback ID, a meta feedback type, andmeta feedback.

The meta feedback ID is an ID for identifying individual meta feedback.

The user ID is a user ID of the user that has given the meta feedback.

The target feedback ID corresponds to the feedback ID of the feedback DB23 of FIG. 6, and indicates the feedback ID that is a target of the metafeedback. That is, the target feedback ID indicates the feedback ID ofthe feedback DB 23 corresponding to the CUF tuple to which the metafeedback is given.

The meta feedback type indicates the type of the given meta feedback,and is classified into “character string,” “operation,”“thumbs-up/down,” “purchase,” and so forth, for example. For example,the meta feedback using character information such as a comment isclassified into “character string”. The meta feedback using the useroperation such as view is classified into “operation.” The meta feedbackusing the thumbs-up button 106 and the thumbs-down button 107 of FIG. 4is classified into “thumbs-up/down.” The meta feedback using the contentpurchase is classified into “purchase.”

The meta feedback item indicates contents of the meta feedback actuallygiven by the user.

For example, information indicating that the user U1 has given the metafeedback of the character string type such as “I agree with thatopinion!” on the CUF tuple in which the feedback ID of the feedback DB23 is the record F1 is stored in the record MF1 of the meta feedback IDof FIG. 7. Information indicating that the user U1 has carried out the“reproduction” operation on the CUF tuple in which the feedback ID ofthe feedback DB 23 is the record F2 is stored in the record MF2 of themeta feedback ID. Information indicating that the user U2 has given the“thumbs-down” evaluation on the CUF tuple in which the feedback ID ofthe feedback DB 23 is the record F4 is stored in the record MF3 of themeta feedback ID. Information indicating that the user U2 has carriedout the content “purchase” on the CUF tuple in which the feedback ID ofthe feedback DB 23 is the record F5 is stored in the record MF4 of themeta feedback ID.

In step S3, the feedback analysis unit 25 carries out feedback analysis.In particular, the feedback analysis unit 25 extracts feature elementsfrom the feedback stored in each record of the feedback DB 23 as featurequantities. For example, the feedback analysis unit 25 uses morphemeanalysis, syntax analysis, or the like to extract feature words from thefeedback using natural language such as the character string type asfeature quantities. The feedback analysis unit 25 then stores theextracted feature quantities in the corresponding records of thefeedback DB 23.

FIG. 8 illustrates an example after the feedback analysis is carried outon the feedback DB 23 of FIG. 6. For example, two feature quantities of“cool” and “!” are extracted from the feedback “This is cool!” in therecord F1 of the feedback ID.

The feedback analysis unit 25 also carries out a similar analysisprocess on the meta feedback DB 24.

FIG. 9 illustrates an example after the feedback analysis is carried outon the meta feedback DB 24 of FIG. 7. For example, three featurequantities of “opinion,” “agree,” and “!” are extracted from the metafeedback “I agree with that opinion!” in the record MF1 of the metafeedback ID.

In step S4, the feedback discrimination unit 26 carries out feedbackdiscrimination. For example, the feedback discrimination unit 26discriminates whether the meta feedback of each record of the metafeedback DB 24 is positive indicating agreement or negative indicatingobjection to the CUF tuple that is a target. In other words, thefeedback discrimination unit 26 discriminates whether or not the userhas accepted the CUF tuple that is the target.

For example, with regard to the meta feedback using the 5-phaseevaluation or likes/dislikes, the feedback discrimination unit 26 usesvalues of the meta feedback as is to carry out the positive or negativediscrimination.

In addition, for example, with regard to the meta feedback using thenatural language, the feedback discrimination unit 26 usespositive/negative discrimination techniques or the like of subjectiverepresentation based on the feature quantities extracted from thecorresponding meta feedback to carry out the positive or negativediscrimination. In addition, for example, details of thepositive/negative discrimination techniques of subjective representationare disclosed in N. Kobayashi, “Opinion Mining from Web Documents:Extraction and Structurization”, Journal of artificial intelligence,Vol. 22, No. 2, 2007, pp. 227-238.

In addition, for example, the feedback control unit 25 considerscorrelation with other meta feedback or carries out the positive ornegative discrimination in accordance with a predetermined rule withregard to the meta feedback using the implicit feedback. In the lattercase, for example, the meta feedback is classified as positive when themeta feedback is “Reproduction” or “Thumbs-up,” and is classified asnegative when the meta feedback is “Stop,” “Skip,” or “Thumbs-down.”

The feedback discrimination unit 26 then stores the discriminationresult in each record of the meta feedback DB 24.

FIG. 10 illustrates an example after feedback discrimination is carriedout on the meta feedback DB 24 of FIG. 9. For example, the meta feedbacksuch as “I agree with that opinion!” “Reproduction,” and “Purchase” areclassified as positive, and the meta feedback such as “Thumbs-down” isclassified as negative.

In addition to the simple positive or negative discrimination, forexample, positive and negative degrees of the discrimination may bedetermined and these degrees may be numerically represented.

In addition, the discrimination result is supervised data at the time ofgenerating a next acceptance model.

In step S5, the acceptance model generation unit 51 generates theacceptance model. That is, the acceptance model generation unit 51 usesthe information stored in the feedback DB 23, the meta feedback DB 24,the user DB 27, and the content metadata DB 28 to generate theacceptance model with respect to the tuple of each type.

For example, the acceptance model for the CUF tuple is a model forpredicting to what degree each user will accept each combination of thecontent, the user, and the feedback. In addition to the CUF tuple, theacceptance models are generated for CU, UF, CF, C, U, and F tuples.

A method of generating the acceptance model broadly includes a methodusing collaborative filtering and a method using CBF.

In addition, an example of the method using the collaborative filteringwill be described.

For example, the collaborative filtering is defined as a predictionproblem of a matrix including non-observed elements using the user andthe content as a row and a column as described in Non-Patent Document 1(Zheng, V. W., et al., “Collaborative Filtering Meets MobileRecommendation: A User-centered Approach”, AAAI, 2010), and additionalinformation (metadata) associated with the user or the content is notused.

For example, the collaborative filtering for generating the acceptancemodel on the CUF tuple is a prediction problem of the four-dimensionalarrangement (tensor) including four elements such as a user on theacceptance side (active user) added to a content, a user, and feedback.On the other hand, the collaborative filtering for generating theacceptance model on the F tuple is a prediction problem of thetwo-dimensional arrangement (matrix) including two elements such as thefeedback and the user on the acceptance side (active user).

To predict the elements within the tensor, for example, methodsdescribed in Non-Patent Document 2 (Zheng, V. W., et al., “CollaborativeFiltering Meets Mobile Recommendation: A User-centered Approach”, AAAI,2010), Non-Patent Document 3 (Symeonidis, P., et al., “TagRecommendations Based on Tensor Dimensionality Reduction”, Proceedingsof Recommender Systems, 2008), and Non-Patent Document 4 (Kolda, T. G.,Bader, B. W., “Tensor Decompositions and Applications”, SIAM Review,2008) may be employed.

For example, cases in which active users A1 to A3 (corresponding to therespective users U1 to U3) give meta feedback on the CU tuple (Cε{C1,C2, C3, 4}, Uε{U1, U2, U3, U4}) as shown in respective FIGS. 11 to 13will be described.

In addition, as a value indicated in the respective items, 1 correspondsto positive meta feedback being given, −1 corresponds to negative metafeedback being given, and 0 corresponds to no meta feedback being given.For example, it is seen in FIG. 11 that the active user A1 gives no metafeedback on the (C1, U1) tuple, positive meta feedback on the (C1, U2)tuple, and negative meta feedback on the (C1, U4) tuple.

When giving the meta feedback as the value described above is carriedout in the active user direction, used as a third tensor of 3×4×4, andapplied to the CANDECOMP/PARAFAC decomposition described in Non-PatentDocument 4, results are obtained as shown in FIGS. 14 to 17.

In addition, FIG. 14 illustrates a two-dimensional latent vector withrespect to the active users A1 to A3. FIG. 15 illustrates atwo-dimensional latent vector with respect to the contents C1 to C4.FIG. 16 illustrates a two-dimensional latent vector with respect to theusers U1 to U4 that have given feedback. FIG. 17 illustrates atwo-dimensional weight vector of Equation 1 that will be describedlater.

Here, when elements of each matrix are {a_(ir)}, {c_(jf)}, {u_(kr)}, andλ=[λ₁, λ₂], the value x_(ijk) of the original tensor is approximated asin Equation 1 below:

$\begin{matrix}{x_{ijk} = {\sum\limits_{r = 1}^{2}\; {\lambda_{r}a_{ir}b_{jr}c_{kr}}}} & (1)\end{matrix}$

Equation 1 is the acceptance model. That is, the calculated value inEquation 1 is the predicted acceptance degree of the active user withrespect to the CUF tuple to which the meta feedback is not given in theoriginal tensor. The possibility that the active user will accept theCUF tuple as the target is thus high when the predicted acceptancedegree is high, and is low when the predicted acceptance degree is low.

Next, an example of the method using the CBF will be described.

In the method using the CBF, for example, the acceptance model isgenerated based on the feature quantity of the user stored in the userDB 27, the feature quantity (metadata) of the content stored in thecontent metadata DB 28, and the feature quantity of the feedbackdescribed above.

For example, all feature quantities described above are used as onevector, the positive or negative discrimination result from step S4 isused as a positive or negative example, and discrimination techniques(e.g., see Non-Patent Document 5: Bishop C. M., “Pattern Recognition andMachine Learning”, Springer-Verlag, 2006) such as a support vectormachine or logistic regression are applied, thereby obtaining theacceptance model.

In addition, when the feedback discrimination result has three or morevalues such as the 5-phase, linear regression or the like is used. Inaddition, for example, when contents of the feedback are ignored,discrimination is carried out on a vector space only having featurequantities of the CUF tuple in a similar way to the case of thecollaborative filtering.

For example, the feature quantity vector of the CUF tuple is representedas shown in FIG. 18.

In addition, values in the respective items of FIG. 18 indicate featurequantities of the respective items shown in the second or higher columnof the content, the user, and the feedback included in the evaluationtarget tuple shown in the first column. For example, the record in thefirst line of FIG. 18 indicates the feature quantities of the content,the user, and the feedback included in the (C1, U2, F1) tuple that is anevaluation target tuple. In particular, the feature quantities of theitems such as “genre rock,” “genre pops,” “genre jazz,” “tempo,”“volume,” and “rhythm instrument ratio” of the content C1 are 1, 0, 0,40, 55, and 40, respectively. The feature quantities of the items suchas “male,” “female,” “twenties or lower,” “thirties,” and “forties orhigher” of the user U1 are 1, 0, 0, 1, and 0, respectively. The featurequantities of the items such as “cool,” “!”, feature quantity 4, andfeature quantity 5 of the feedback F1 are 1, 1, 0, and 0, respectively.

Learning is then carried out using logistic regression or the like basedon the discrimination result of the meta feedback, thereby obtaining theweight for each feature for calculating the predicted acceptance degreewith respect to the CUF tuple of each active user as shown in FIG. 19.

In addition, values of the respective items of FIG. 19 indicate weightsfor the respective items shown in the second or higher column of theactive user shown in the first column. For example, the first record ofFIG. 19 indicates the weight with respect to each item of the activeuser A1. In particular, the weights of the items such as “genre rock,”“genre pops,” “genre jazz,” “tempo,” “volume,” and “rhythm instrumentratio” associated with the content of the active user A1 are 0.85, 0.20,−0.42, 0.021, 0.152, and 0.002, respectively. The weights of the itemssuch as “male,” “female,” “twenties or lower,” “thirties,” and “fortiesor higher” associated with the user of the active user A1 are 0.51,0.22, 0.11, 0.53, and 0.33, respectively. The weights of the featuressuch as “cool,” “!”, feature quantity 4, and feature quantity 5associated with the feedback of the active user A1 are 0.79, 0.35, 1.24,and 0.80, respectively.

An addition equation using each weight of FIG. 19 then becomes anacceptance model. That is, values that are added by multiplying thefeature quantities of the content, the user, and the feedback includedin the CUF tuple by the corresponding respective weights of FIG. 19 arepredicted acceptance degrees of the active user with respect to the CUFtuple.

In addition, the method described above is an example of the method ofgenerating the acceptance model, and other methods may be employed. Forexample, as described in Non-Patent Document 6 (Agarwal, D., Chen,B.-C., “Regression-based Latent Factor models,” KDD, 2009), it ispossible to use a method combining characteristics of both thecollaborative filtering and the CBF.

In addition, the acceptance models for the CU tuple, the UF tuple, theCF tuple, the C tuple, the U tuple, and the F tuple are also generatedin a similar way to the case of the CUF tuple.

The acceptance model generation unit 51 then stores the generatedacceptance model in the acceptance model DB 52.

The process is then finished.

In addition, for example, the acceptance model generation process may becarried out whenever the processes of step S1 to S4 are carried out,whenever the feedback is collected for a given quantity, or whenever agiven period has elapsed.

In addition, the description above corresponds to the example ofautomatically generating the acceptance model, but the acceptance modelmay be manually generated and stored in the acceptance model DB 52 inaccordance with a predetermined rule.

(Content Recommendation Process 1)

Next, a first embodiment of the content recommendation process 1 carriedout by the server 11 will be described with reference to the flowchartof FIG. 20.

In addition, in this process, the tuple having a high acceptancepossibility for each active user is found and presented.

In step S101, the acceptance prediction unit 53 carries out acceptanceprediction. In particular, the acceptance prediction unit 53 firstselects the type of the tuple on which the acceptance prediction iscarried out among the types excluding the C tuple, in other words,selects the type of the tuple used for recommending the content. Onetype of the CUF tuple, the CF tuple, the CU tuple, the UF tuple, the Utuple, and the F tuple is thus selected.

Next, the acceptance prediction unit 53 carries out the acceptanceprediction on the active user that receives content recommendation usingthe acceptance model stored in the acceptance model DB 52 for each tupleof the selected type. As a result, the predicted acceptance degree ofthe active user with respect to each tuple of the selected type iscalculated.

In addition, here, the only tuple having data in the feedback DB 23 isthe prediction target. The tuple including the user that does notactually give the feedback or the feedback that is not actually given isthus excluded from the prediction target. For example, when the user U1gives the feedback on the content C1 and does not give the feedback onthe content C2, the (C1, U1) tuple is the prediction target, and the(C2, U1) tuple is excluded from the prediction target.

The acceptance prediction unit 53 then notifies the presentation tupleselection unit 33 of the prediction result.

FIG. 21 illustrates an example of the acceptance prediction result withrespect to the CUF tuple when the active user is the user U1. Forexample, the predicted acceptance degree of the active user U1 withrespect to the (C1, U2, F2) tuple is shown as 0.32.

In addition, hereinafter, the process when the acceptance predictionresult of FIG. 21 is obtained using step S101 will be specificallydescribed.

In step S102, the presentation tuple selection unit 33 selects apresentation tuple.

For example, the presentation tuple selection unit 33 selects apredetermined number of tuples in descending order of higher predictedacceptance degree as the presentation tuples. That is, the presentationtuple selection unit 33 preferentially selects the tuple having thehigher predicted acceptance degree as the presentation tuple.

For example, when two presentation tuples are selected using theacceptance prediction result of FIG. 21, the (C1, U3, F3) tuple havingthe highest predicted acceptance degree of 0.88 and the (C5, U4, F6)tuple having the next highest predicted acceptance degree of 0.67 areselected.

Alternatively, the content recommended by an acquaintance or a person ofinterest tends to be readily accepted. A user associated with the activeuser may then be searched using the user network DB 32 and a tupleincluding the searched user may be preferentially selected as thepresentation tuple.

For example, a predetermined weight is added to the predicted acceptancedegree of the tuple including the user associated with the active user,and the presentation tuple may be selected based on the predictedacceptance degree to which the weight is added.

For example, in the user network DB 32 of FIG. 2, the user U1 as theactive user follows the user U2 and the user U5. Accordingly, forexample, when the weight is set to 0.5, the predicted acceptance degreewith respect to the (C1, U2, F2) tuple and the (C3, U2, F4) tupleincluding the user U2 among the predicted acceptance degrees of FIG. 21are added by the weights, thereby obtaining 0.82 and 0.60, respectively.Accordingly, when two presentation tuples are selected, the (C1, U3, F3)tuple having the highest predicted acceptance degree of 0.88 and the(C1, U2, F2) tuple having the next highest predicted acceptance degreeof 0.82 are selected.

In step S103, the presentation tuple selection unit 33 selects thecontent to be recommended to the active user. In particular, when thepresentation tuple selected in the process of step S102 is the UF tupleor the F tuple, the content that is a target typically exists in thefeedback included in the presentation tuple. The presentation tupleselection unit 33 then selects the content that is a target of thefeedback included in the presentation tuple as the content to berecommended to the active user. The presentation tuple selection unit 33then adds the selected content to the presentation tuple (UF tuple or Ftuple). As a result, the presentation tuple is the CUF tuple or the CFtuple.

In addition, when the presentation tuple selected in step S102 is the Utuple, the number of content on which the user included in the selectedtuple (hereinafter referred to as a presentation user in this process)gives the feedback is not necessarily limited to one. The presentationtuple selection unit 33 thus selects the content to be recommended tothe active user among the contents on which the presentation user givesthe feedback.

For example, all of the contents on which the presentation user givesthe feedback may be selected or the predetermined number of contentsamong all of the contents may be randomly selected. Alternatively, amongthe contents on which the presentation user gives the feedback, thecontent registered in the content promotion DB 31 may be preferentiallyselected. Alternatively, when a sales promotion cost is paid to theservice provider by the content provider, the content having a highsales promotion cost may be preferentially selected among the contentson which the presentation user gives the feedback.

In addition, in this case, only the content on which the presentationuser gives the positive feedback may be used as the target.

The presentation tuple selection unit 33 then adds the selected contentto the presentation tuple (U tuple). As a result, the presentation tuplebecomes the CU tuple.

In addition, when the presentation tuple selected in the process of stepS102 is any one of the CUF tuple, the CU tuple, and the CF tuple, thecontent is already included in the presentation tuple. The contentincluded in the presentation tuple is thus the content to be recommendedto the active user as is.

The presentation tuple selection unit 33 then notifies the presentationcontrol unit 34 of the presentation tuple.

In step S104, the server 11 presents the content. In particular, thepresentation control unit 34 acquires information (metadata) associatedwith the content included in the presentation tuple from the metadata DB28. In addition, when a user is included in the presentation tuple, thepresentation control unit 34 acquires information associated with theuser from the user DB 27. In addition, when feedback is included in thepresentation tuple, the presentation control unit 34 acquiresinformation associated with the feedback from the feedback DB 23.

The presentation control unit 34 generates display data for displaying acontent recommendation screen for recommending the content to the activeuser based on the acquired information. The presentation control unit 34then notifies the user terminal 12 of the active user of the generateddisplay data via the communication unit 21 and the network 13.

The user terminal 12 of the active user displays the contentrecommendation screen based on the display data received from the server11.

FIGS. 22 to 24 illustrate examples of the content recommendation screen.

FIG. 22 illustrates an example of the content recommendation screen whenthe presentation tuple is the CUF tuple. In particular, a content name(content A) is displayed in the upper left corner, and an image 201indicating the content is displayed below the content name.

In addition, information associated with the feedback on the content Ais displayed on the right side of the display described above. That is,the image 202 a indicating the user A that has given the feedback on thecontent A, a user name of the user A, and a balloon 203 a includingcontents (e.g., comment or the like) of the feedback of the user A aredisplayed. In a similar way, an image 202 b indicating a user B that hasgiven the feedback on the content A, a user name of the user B, and aballoon 203 b including contents (e.g., a comment or the like) of thefeedback of the user B are displayed.

FIG. 23 illustrates an example of the content recommendation screen whenthe presentation tuple is the CF tuple. In this content recommendationscreen, the images 202 a and 202 b and the user names of the user A andthe user B are not displayed in comparison with the contentrecommendation screen of FIG. 22. That is, information associated withthe user that has given the feedback on the content A is not displayedand only contents of the feedback are displayed in the contentrecommendation screen.

FIG. 24 illustrates an example of the content recommendation screen whenthe presentation tuple is the CU tuple. In this content recommendationscreen, the balloons 203 a and 203 b are not displayed in comparisonwith the content recommendation screen of FIG. 22. That is, contents ofthe feedback given to the content A are not displayed and onlyinformation associated with the user that has given the feedback isdisplayed in the content recommendation screen.

FIGS. 25 to 27 illustrate examples of a content recommendation screenwhen the recommended content is displayed in a list form.

FIG. 25 illustrates an example of the content recommendation screen in alist form when the presentation tuple is the CUF tuple. In this case,information associated with the content A is displayed in the firstline, and information associated with the content B is displayed in thesecond line. In particular, an image 221 a indicating the content A, acontent name of the content A, an image 222 a and a user name indicatingthe user A that has given the feedback on the content A, and a balloon223 a including the feedback on the content A of the user A aredisplayed in order from the left in the first line. Similarly, an image221 b indicating the content B, a content name of the content B, animage 222 b and a user name indicating the user B that has given thefeedback on the content B, and a balloon 223 b including the feedback onthe content B of the user B are displayed in order from the left in thesecond line.

FIG. 26 illustrates an example of the content recommendation screen in alist from when the presentation tuple is the CF tuple. In this contentrecommendation screen, the images 222 a and 222 b and the user names ofthe user A and the user B are not displayed in comparison with thecontent recommendation screen of FIG. 25. Instead, a balloon 223 cincluding the feedback on the content A and a balloon 223 d includingthe feedback on the content B are added. That is, information associatedwith the user that has given the feedback on each content is notdisplayed and only contents of the feedback are displayed in the contentrecommendation screen.

FIG. 27 illustrates an example of the content recommendation screen whenthe presentation tuple is the CU tuple. In this content recommendationscreen, the balloons 223 a and 223 b are not displayed in comparisonwith the content recommendation screen of FIG. 25. Instead, an image 222c indicating the user C that has given the feedback on the content A anda user name are added. That is, contents of the feedback given to eachcontent are not displayed and only information associated with the userthat has given the feedback is displayed in the content recommendationscreen.

In addition, for example, when the user U and the feedback F areincluded in the presentation tuple, information such as “This is thecontent in which User U has said ‘feedback F,’” may be displayed. Inthis case, for example, a user associated with the active user andfeedback of the user may be preferentially displayed.

In addition, for example, when only the feedback F is included and theuser U is not included in the presentation tuple, information “This isthe content in which ‘feedback F’ was said,” may be displayed.

In addition, for example, when only the user U is included and thefeedback F is not included in the presentation tuple, information “Thisis the content that User U said,” may be displayed. In this case, forexample, the user associated with the active user may be preferentiallydisplayed.

In addition, images indicating users or utterance contents may beswitched and displayed in accordance with contents or the like of thefeedback words in the content recommendation screens of FIGS. 22 to 27.

The process is then finished.

In this way, the CUF tuple, the CF tuple, or the CU tuple having a highpredicted acceptance degree is preferentially presented to the activeuser. The possibility that the active user will accept the presentedcontent is thus high.

In addition, since the content and at least one of the user that givesthe feedback on the content and the feedback on the content arepresented, the user can know the content recommendation reason.

In addition, when the feedback is presented, since the contents of thefeedback given by other user are presented as is, the recommendationreason is more clearly conveyed to the active user.

(Content recommendation process 2)

Next, a second embodiment of the content recommendation process 2carried out by the server 11 will be described with reference to theflowchart of FIG. 28.

In addition, this process finds an influential user that causes theactive user to readily accept the utterance or the like, prompts thefound influential user to give feedback on the content, and presents thecorresponding user or the corresponding feedback along with thecorresponding content to the active user.

In step S121, the presentation tuple selection unit 33 selects thecontent to be recommended to the active user. For example, thepresentation tuple selection unit 33 selects a predetermined number ofcontents among the contents registered in the content promotion DB 31 asthe contents to be recommended to the active user (hereinafter referredto as a recommendation content).

In step S122, the presentation tuple selection unit 33 carries outacceptance prediction. In particular, the acceptance prediction unit 53carries out the acceptance prediction on the active user with respect tothe CU tuple or the U tuple using a similar process to step S101 of FIG.20.

In addition, when the acceptance prediction is carried out on the CUtuple, the CU tuple becomes a prediction target when it includes therecommendation content.

The acceptance prediction unit 53 then notifies the presentation tupleselection unit 33 of the prediction result.

In step S123, the presentation tuple is selected in a similar way tostep S102 of FIG. 21. However, when the acceptance prediction is carriedout on the U tuple, the presentation tuple selection unit 33 sets the CUtuple in which the recommendation content is added to the selectedpresentation tuple (U tuple) as the presentation tuple.

This causes the CU tuple having a higher predicted acceptance degree ofthe active user to be preferentially selected as the presentation tuplewhen the acceptance prediction is carried out on the CU tuple. On theother hand, when the acceptance prediction is carried out on the Utuple, the CU tuple including the user (U tuple) having a higherpredicted acceptance degree of the active user is selected as thepresentation tuple.

The presentation tuple selection unit 33 then notifies the presentationcontrol unit 34 and the feedback guidance unit 35 of the presentationtuple.

In addition, hereinafter, a user included in the presentation tuple isreferred to as a recommender in the process.

In step S124, the feedback guidance unit 35 prompts the recommender togive feedback on the recommendation content. In particular, the feedbackguidance unit 35 acquires information (metadata) associated with therecommendation content from the content metadata DB 28. The feedbackguidance unit 35 generates display data for displaying a feedbackguiding screen for prompting the recommender to give feedback on therecommendation content based on the acquired information. The feedbackguidance unit 35 then transmits the generated display data to the userterminal 12 of the recommender via the communication unit 21 and thenetwork 13.

The user terminal 12 of the recommender displays the feedback guidingscreen based on the display data received from the server. For example,the user terminal 12 displays information of the recommendation contentand a message such as “How about recommending this to Mr. X (activeuser)?” prompting the recommender to give feedback.

When feedback is given on the recommendation content by the recommender,the user terminal 12 of the recommender transmits feedback informationassociated with the given feedback to the server 11 via the network 13.The feedback collection unit 22 of the server 11 receives the feedbackinformation via the communication unit 21 and stores the feedbackinformation in the feedback DB 23.

In step S125, the server 11 presents the content. In particular, thepresentation control unit 34 acquires information associated with therecommendation content included in the presentation tuple from thecontent metadata DB 28. In addition, the presentation control unit 34acquires information associated with the recommender from the user DB27. In addition, the presentation control unit 34 acquires informationassociated with the feedback on the recommendation content of therecommender from the feedback DB 23.

The presentation control unit 34 generates display data for displayingthe content recommendation screen for recommending the content to theactive user based on the acquired information. The presentation controlunit 34 then transmits the generated display data to the user terminal12 of the active user via the communication unit 21 and the network 13.

The user terminal 12 of the active user displays the contentrecommendation screen based on the display data received from the server11.

In this case, it is considered to be important for the active user thatthe recommender give feedback. Accordingly, for example, informationassociated with the content along with words such as “This is thecontent that User B (recommender) said,” may be displayed withoutdisplaying the feedback of the recommender.

On the other hand, only the feedback of the recommender may be presentedalong with the information associated with the content withoutpresenting the recommender.

The process is then finished.

In this way, since the influential user that causes the active user toreadily accept the content is presented along with the content as therecommender, the possibility that the active user will accept thepresented content becomes higher.

In addition, when the recommender has already given the feedback on therecommendation content, it is possible to skip step S124.

Alternatively, for example, the CU tuple including the user that givesthe feedback on the recommendation content may be excluded fromacceptance prediction targets or presentation tuple targets. This causesa tuple including a user that has not yet given feedback on therecommendation content to be selected as the presentation tuple.

In addition, for example, in step S124, the recommender may not beexplicitly guided to give feedback, the recommendation content may besimply recommended to the recommender, and the provision of the feedbackmay be awaited.

(Content recommendation process 3)

Next, a third embodiment of the content recommendation process 3 carriedout by the server 11 will be described with reference to the flowchartof FIG. 29.

In addition, this process finds a user or feedback that is readilyaccepted by many active users, and preferentially presents the founduser or feedback along with the content.

In step S141, the acceptance prediction unit 53 carries out acceptanceprediction. In particular, the acceptance prediction unit 53 carries outthe acceptance prediction of a plurality of users (active users) on anyone of the CUF tuple, the CF tuple, the CU tuple, the UF tuple, the Utuple, and the F tuple using a similar process to step S101 of FIG. 20.The acceptance prediction unit 53 notifies the prediction counting unit30 of the prediction result.

In addition, the active users that are targets carrying out theacceptance prediction may be system-wide users or a specific partialuser group.

In addition, here, the only tuple having data in the feedback DB 23 is aprediction target. A tuple including a user that does not actually givefeedback or feedback that is not actually given is thus excluded fromthe prediction target.

In step S142, the prediction counting unit 30 counts prediction results.In particular, the prediction counting unit 30 counts the predictedacceptance degree per user ID (per U tuple), per feedback ID (per Ftuple), or per combination of the user ID and the feedback ID (per UFtuple), and obtains a statistic such as an average value.

Here, a specific example of the counting method will be described in acase in which the acceptance prediction result of the CUF tuple of eachactive user is illustrated as shown in FIG. 30.

In addition, FIG. 30 illustrates the predicted acceptance degrees of theCUF tuples of the active users, respectively. For example, the predictedacceptance degree of the (C1, U2, F102) tuple of the active user U1 isshown as 0.32. In addition, FIG. 31 illustrates the predicted acceptancedegrees sorted by the content ID and the user ID of FIG. 30.

For example, a case in which the acceptance prediction result of FIG. 30is counted for each UF tuple (combination of the user ID and thefeedback ID) will be described.

For example, an average value of the predicted acceptance degrees of therespective active users of the (U2, F102) tuples is 0.463(=(0.32+0.65+0.42)/3). In addition, an average value of the predictedacceptance degrees of the respective active users of the (U3, F103)tuples is 0.643 (=(0.88+0.41+0.64)/3).

In addition, contents that are the targets of the (U2, F102) tuples andthe (U3, F103) tuples are the contents C1.

In addition, for example, an average value of the predicted acceptancedegrees of the respective active users of the (U1, F107) tuples is 0.493(=(0.54+0.60+0.34)/3). In addition, an average value of the predictedacceptance degrees of the respective active users of the (U3, F105)tuples is 0.650 (=(0.54+0.63+0.78)/3).

In addition, contents that are the targets of the (U1, F107) tuples andthe (U3, F105) tuples are the contents C4.

In this way, the average values of the predicted acceptance degrees ofthe respective UF tuples are calculated.

When the UF tuple has a higher average value of the predicted acceptancedegrees, a combination of the user and the feedback included in the UFtuple is easily accepted by more active users. In other words, it can besaid that the combination is a more influential combination with respectto each active user. For example, the (U3, F103) tuple is moreinfluential than the (U2, F102) tuple in terms of the content C1, andthe (U3, F105) tuple is more influential than the (U1, F107) tuple interms of the content C4.

In addition, the counting may be carried out not per UF tuple but per Utuple (per user ID) or per F tuple (per feedback ID).

For example, when the counting is carried out per U tuple, an averagevalue of the predicted acceptance degrees of the respective active usersof the (U2) tuples is 0.455 (=(0.32+0.65+0.42+0.21+0.23+0.9)/6).

When the U tuple has a higher average value of the predicted acceptancedegrees, a user included in the U tuple is readily accepted by moreactive users. In other words, it can be said that the user is a moreinfluential user with respect to each active user.

In addition, when the counting is carried out per F tuple, the feedbackID typically corresponds to one user ID, and is thus equal to thecounted result per UF tuple.

The prediction counting unit 30 then notifies the presentation tupleselection unit 33 of the counted result.

In step S143, the presentation tuple selection unit 33 selects thepresentation tuple based on the counted result.

For example, when the acceptance prediction is counted per UF tuple, thepresentation tuple selection unit 33 selects a predetermined number ofUF tuples in descending order having higher average values of thepredicted acceptance degrees from the UF tuples given to each contentper content. The presentation tuple selection unit 33 then sets the CUFtuple in which the content is added as a target to the selected UF tupleas the presentation tuple. The CUF tuple including the UF tuple havinghigher predicted acceptance degrees of a plurality of active users asacceptance prediction targets is thus preferentially selected as thepresentation tuple.

For example, in the case described above, the (U3, F103) tuple having ahigh predicted acceptance degree is selected among the UF tuples givento the content C1. The (C1, U3, F103) tuple in which the content C1 isadded to the selected (U3, F103) tuple is thus the presentation tuple.

Similarly, when the acceptance prediction is counted per F tuple, thepresentation tuple selection unit 33 selects a predetermined number of Ftuples having higher average values of the predicted acceptance degreesamong the F tuples given to each content per content. The presentationtuple selection unit 33 then sets the CF tuple in which the content isadded as a target to the selected F tuple as the presentation tuple. TheCF tuple including the F tuple having higher predicted acceptancedegrees of a plurality of active users as acceptance prediction targetsis thus preferentially selected as the presentation tuple.

In addition, in this case, the CUF tuple to which the user that hasgiven the feedback included in the F tuple is added may be selected asthe presentation tuple.

On the other hand, when the acceptance prediction is counted per Utuple, the presentation tuple selection unit 33 selects a predeterminednumber of U tuples in descending order having higher average values ofthe predicted acceptance degrees as the presentation tuples. The F tuple(user) having higher predicted acceptance degrees of a plurality ofactive users as acceptance prediction targets is thus preferentiallyselected as the presentation tuple.

The presentation tuple selection unit 33 then notifies the presentationcontrol unit 34 of the presentation tuple.

In step S144, the content is presented by a similar process to step S104of FIG. 20.

For example, when the presentation tuple selected by the presentationtuple selection unit 33 is the CUF tuple, a combination of the content,the user, and the feedback included in each presentation tuple ispresented to the active user. The combination of the more influentialuser with respect to the content and the feedback is thus preferentiallypresented along with each content to each active user.

Similarly, when the presentation tuple selected by the presentationtuple selection unit 33 is the CF tuple, a combination of the contentand the feedback included in each presentation tuple is presented to theactive user. The feedback that is more influential on the content isthus preferentially presented along with each content to each activeuser.

On the other hand, when the presentation tuple selected by thepresentation tuple selection unit 33 is the U tuple, a user included inthe presentation tuple is preferentially presented along with thecontent to be presented to each active user. That is, when feedback isgiven on the content to be presented to each active user by the userincluded in the presentation tuple, a combination of the correspondingcontent and the corresponding user is preferentially presented to eachactive user. The user having a higher influence on the content among theusers that have given feedback on the corresponding contents is thuspreferentially presented along with each content. In addition, in thiscase, feedback given by the corresponding user may also be presentedaccordingly.

The process is then finished.

In this way, when the content is presented to each active user, aninfluential user that causes many active users to readily accept thecontent, feedback, or a combination of the user and the feedback ispreferentially presented. This increases the possibility that eachactive user will accept the presented content.

(Content Recommendation Process 4)

Next, a fourth embodiment of the content recommendation process 4carried out by the server 11 will be described with reference to theflowchart of FIG. 32.

In addition, this process finds an influential user (influencer) thatcauses many users to readily accept the utterance or the like, promptsthe found user to give feedback on the content, and presents thecorresponding user or the corresponding feedback along with thecorresponding content.

In step S161, the acceptance prediction unit 53 carries out acceptanceprediction. In particular, the acceptance prediction unit 53 uses asimilar process to step S101 of FIG. 20 to carry out the acceptanceprediction of a plurality of users (active users) on the CU tuple or theU tuple. The acceptance prediction unit 53 notifies the predictioncounting unit 30 of the prediction result.

In addition, the active users that are targets carrying out theacceptance prediction may be system-wide users or a specific partialuser group.

In step S162, the prediction counting unit 30 counts the predictionresult. In particular, the prediction counting unit 30 counts thepredicted acceptance degrees per user ID (per U tuple), and obtains astatistic such as an average value.

For example, when the acceptance prediction result of FIG. 30 is used,an average value of the predicted acceptance degrees of the respectiveactive users of the U1 tuple (user U1) is 0.493 (=(0.54+0.60+0.34)/3).An average value of the predicted acceptance degrees of the respectiveactive users of the U2 tuple (user U2) is 0.455(=(0.32+0.21+0.65+0.23+0.42+0.90)/6). An average value of the predictedacceptance degrees of the respective active users of the U3 tuple (userU3) is 0.643 (=(0.88+0.41+0.64)/3). An average value of the predictedacceptance degrees of the respective active users of the U4 tuple (userU4) is 0.562 (=(0.54+0.73+0.63+0.15+0.78+0.54)/6).

When the U tuple has a higher average value of the predicted acceptancedegrees, a user included in the U tuple is readily accepted by moreactive users. In other words, it can be said that the user is moreinfluential on each active user.

The prediction counting unit 30 then notifies the presentation tupleselection unit 33 of the counted result.

In step S163, the presentation tuple selection unit 33 selects thepresentation tuple. In particular, the presentation tuple selection unit33 selects a predetermined number of U tuples in descending order havinghigher average values of the predicted acceptance degrees, and the userincluded in the selected U tuple is the recommender. The users includedin the U tuple having higher predicted acceptance degrees of a pluralityof active users as acceptance prediction targets are thus preferentiallyselected as the recommenders.

In addition, the presentation tuple selection unit 33 selects thecontent recommended to each active user. For example, the presentationtuple selection unit 33 selects a predetermined number of contents amongthe contents registered in the content promotion DB 31 as therecommendation contents.

Alternatively, for example, the presentation tuple selection unit 33obtains an average value of the predicted acceptance degrees in each CUtuple including the recommender. The presentation tuple selection unit33 then selects a predetermined number of CU tuples in descending orderhaving high average values of the predicted acceptance degrees, andselects the content included in the selected CU tuple as therecommendation content.

The presentation tuple selection unit 33 sets the CU tuple including acombination of the selected recommender and the recommendation contentas the presentation tuple. The presentation tuple selection unit 33notifies the feedback guidance unit 35 and the presentation control unit34 of the presentation tuple.

In step S164, in a similar way to step S124 of FIG. 28, the recommenderis prompted to give feedback on the recommendation content.

In step S165, in a similar way to step S125 of FIG. 29, the content ispresented. Accordingly, when the recommendation content is presented tothe user terminal 12 of each active user, at least one of therecommender and the feedback given to the recommendation content by therecommender is presented along with the recommendation content.

The process is then finished.

In this way, for example, when content promotion is carried out, ahighly influential user that is readily accepted by many active users orfeedback given by the corresponding user is preferentially presentedalong with the content. This increases the possibility that each activeuser will accept the presented content.

(User Recommendation Process)

Next, the user recommendation process carried out by the server 11 willbe described with reference to the flowchart of FIG. 33.

In addition, this process finds a user that is readily accepted byactive users, in other words, finds the user that easily influences theactive users, and presents the user to the active users.

In step S181, the acceptance prediction unit 53 carries out acceptanceprediction. In particular, the acceptance prediction unit 53 uses asimilar process to step S101 of FIG. 20 to carry out the acceptanceprediction of active users on the tuple including at least the user,that is, the CUF tuple, the CU tuple, the UF tuple, or the U tuple. Theacceptance prediction unit 53 notifies the prediction counting unit 30of the prediction result.

In step S182, the prediction counting unit 30 counts the predictionresult. In particular, the prediction counting unit 30 uses a similarprocess to step S142 of FIG. 29 to obtain an average value of thepredicted acceptance degrees per user ID (U tuple ID). The predictioncounting unit 30 notifies the presentation tuple selection unit 33 ofthe counted result.

In step S183, the presentation tuple selection unit 33 selects the userto be recommended to the active user. In particular, the presentationtuple selection unit 33 selects a predetermined number of U tuples indescending order having higher predicted acceptance degrees as thepresentation tuples. The users included in the presentation tuples arethus users to be recommended to the active users (hereinafter referredto as recommendation users in this process). The users included in the Utuples having higher predicted acceptance degrees are thuspreferentially selected as the recommendation users.

The presentation tuple selection unit 33 then notifies the presentationcontrol unit 34 of the presentation tuples.

In step S184, the server 11 recommends the user to the active user. Inparticular, the presentation control unit 34 acquires informationassociated with the recommender included in the presentation tuple fromthe user DB 27. In addition, the presentation control unit 34 acquiresinformation associated with the feedback given by the recommendationuser from the information stored in the feedback DB 23. In addition, thepresentation control unit 34 acquires information (metadata) associatedwith the content as the target of the acquired feedback from the contentmetadata DB 28.

The presentation control unit 34 generates display data for displaying auser recommendation screen for recommending the recommendation user tothe active user based on the acquired information. The presentationcontrol unit 34 then transmits the generated display data to the userterminal 12 of the active user via the communication unit 21 and thenetwork 13.

The user terminal 12 of the active user displays the user recommendationscreen based on the display data received from the server 11. In thiscase, at least a portion of the content on which the feedback is givenby the recommendation user and contents of the feedback is presentedalong with information associated with the recommendation user.

The process is then finished.

In this way, it is possible to recommend the user that is readilyaccepted by the active user.

As described above, for example, the active user can carry outdetermination such as purchasing or using unknown content as well asproper feedback information on other users.

In addition, a service provider can expect the active user to acceptmore contents or users, and purchase and use opportunities of contentsor services to increase.

2. Modified Examples

Hereinafter, modified examples of the embodiments of the presentdisclosure will be described.

Modified Example 1

In the description above, when the acceptance prediction is counted, anaverage value of the predicted acceptance degrees is calculated, and thepresentation tuple or the like is selected based on the average value.However, a statistic other than the average value may be employed. Forexample, a sum, a maximum, or a variance of the predicted acceptancedegrees may be used, or a combination of a plurality of statistics maybe used.

Modified Example 2

In addition, in the description above, the acceptance model is generatedand the predicted acceptance degree is obtained based on the metafeedback on the CUF tuple. However, the present disclosure may use themeta feedback on the CF tuple, for example.

That is, the content and the meta feedback on the feedback on thecorresponding content may be collected, the acceptance model may begenerated and the predicted acceptance degrees may be obtained based onthe collected meta feedbacks. In this case, it is possible to carry outthe process using the predicted acceptance degree with respect to the CFtuple or the F tuple among the content recommendation processesdescribed above.

Modified Example 3

In addition, in the description above, the tuples are selected indescending order having higher predicted acceptance degrees when thepresentation tuple is selected. However, other methods may be employedto preferentially select the tuples having higher predicted acceptancedegrees. For example, the tuples having the predicted acceptance degreesgreater than or equal to a threshold may be selected.

[Configuration Example of Computer]

The series of processes described above can be executed by hardware butcan also be executed by software. When the series of processes isexecuted by software, a program that constructs such software isinstalled into a computer. Here, the expression “computer” includes acomputer in which dedicated hardware is incorporated and ageneral-purpose personal computer or the like that is capable ofexecuting various functions when various programs are installed.

FIG. 34 is a block diagram showing a configuration example of thehardware of a computer that executes the series of processes describedearlier according to a program.

In the computer, a central processing unit (CPU) 401, a read only memory(ROM) 402 and a random access memory (RAM) 403 are mutually connected bya bus 404.

An input/output interface 405 is also connected to the bus 404. An inputunit 406, an output unit 407, a storage unit 408, a communication unit419, and a drive 410 are connected to the input/output interface 405.

The input unit 406 is configured from a keyboard, a mouse, a microphoneor the like. The output unit 407 configured from a display, a speaker orthe like. The storage unit 408 is configured from a hard disk, anon-volatile memory or the like. The communication unit 419 isconfigured from a network interface or the like.

The drive 410 drives a removable media 411 such as a magnetic disk, anoptical disk, a magneto-optical disk, a semiconductor memory or thelike.

In the computer configured as described above, the CPU 401 loads aprogram that is stored, for example, in the storage unit 408 onto theRAM 403 via the input/output interface 405 and the bus 404, and executesthe program. Thus, the above-described series of processing isperformed.

Programs to be executed by the computer (the CPU 401) are provided beingrecorded in the removable media 411 which is a packaged media or thelike. Also, programs may be provided via a wired or wirelesstransmission medium, such as a local area network, the Internet ordigital satellite broadcasting.

In the computer, by inserting the removable media 411 into the drive410, the program can be installed in the storage unit 408 via theinput/output interface 405. Further, the program can be received by thecommunication unit 419 via a wired or wireless transmission media andinstalled in the storage unit 408. Moreover, the program can beinstalled in advance in the ROM 402 or the storage unit 408.

It should be noted that the program executed by a computer may be aprogram that is processed in time series according to the sequencedescribed in this specification or a program that is processed inparallel or at necessary timing such as upon calling.

In the present disclosure, the term “system” means a general apparatusthat is configured using a plurality of devices and mechanisms.

It should be understood by those skilled in the art that variousmodifications, combinations, sub-combinations and alterations may occurdepending on design requirements and other factors insofar as they arewithin the scope of the appended claims or the equivalents thereof.

Furthermore, the present technology may also be configured as follows,for example.

(1)

An information processing device including:

a collection unit configured to collect meta feedback that is feedbackon a combination including a content, a user, and feedback on thecontent of the user; and

a prediction unit configured to obtain a predicted acceptance degreethat is a prediction value of a degree to which an active user that is atarget user accepts a combination including at least one of the content,the user, and the feedback, based on the collected meta feedback.

(2)

The information processing device according to (1), further including:

a selection unit configured to select a combination including a contentto be presented to the active user and at least one of the user that hasgiven feedback on the content and the feedback, based on the predictedacceptance degree; and

a presentation control unit configured to control the content includedin the selected combination and at least one of the user and thefeedback included in the selected combination to be presented to theactive user.

(3)

The information processing device according to (2), wherein theselection unit preferentially selects a combination having a higherpredicted acceptance degree.

(4)

The information processing device according to (1), further including:

a selection unit configured to select a combination of the content andthe user that prompts provision of feedback based on the predictedacceptance degree of the active user with respect to the combination ofthe content and the user or the predicted acceptance degree of theactive user with respect to the user;

a guidance unit configured to prompt the user included in the selectedcombination to give feedback on the content included in the selectedcombination;

and

a presentation control unit configured to control the content includedin the selected combination and at least one of the user included in theselected combination and the feedback given by the user to be presentedto the active user.

(5)

The information processing device according to (4), wherein theselection unit preferentially selects a combination of the content andthe user having a higher predicted acceptance degree or a combination ofthe user and the content including the user having the higher predictedacceptance degree.

(6)

The information processing device according to (1), further including:

a counting unit configured to count the predicted acceptance degrees ofa plurality of active users for each user, for each feedback, or foreach combination of the user and the feedback;

a selection unit configured to select a combination including a contentto be presented and at least one of the user and the feedback, based onthe counted result of the predicted acceptance degrees; and

a presentation control unit configured to control the content includedin the selected combination and at least one of the user and thefeedback included in the selected combination to be presented.

(7)

The information processing device according to (6), wherein theselection unit preferentially selects the combination including the userand the feedback having higher predicted acceptance degrees of theplurality of active users.

(8)

The information processing device according to (1), further including:

a counting unit configured to count the predicted acceptance degrees ofa plurality of active users with respect to a combination of the contentand the user or the predicted acceptance degrees of the plurality ofactive users with respect to the user for each user;

a selection unit configured to select a combination of the content andthe user that prompts provision of feedback, based on the counted resultof the predicted acceptance degrees;

a guidance unit configured to prompt the user included in the selectedcombination to give feedback on the content included in the selectedcombination; and

a presentation control unit configured to control the content includedin the selected combination and at least one of the user included in theselected combination and the feedback given by the user to be presented.

(9)

The information processing device according to (8), wherein theselection unit preferentially selects a combination including the userhaving higher predicted acceptance degrees of the plurality of activeusers.

(10)

The information processing device according to (1), further including:

a counting unit configured to count the predicted acceptance degree ofthe active user with respect to the combination including at least theuser for each user;

a selection unit configured to preferentially select the user having ahigher predicted acceptance degree of the active user; and apresentation control unit configured to control the selected user to bepresented to the active user.

(11)

The information processing device according to any of (1) to (10),wherein the prediction unit includes:

an acceptance model generation unit configured to generate an acceptancemodel for obtaining the predicted acceptance degree, based on thecollected meta feedback; and

an acceptance prediction unit configured to obtain the predictedacceptance degree based on the acceptance model.

(12)

An information processing method including:

collecting, by an information processing device, meta feedback that isfeedback on a combination including a content, a user, and feedback onthe content of the user; and

obtaining, by the information processing device, a predicted acceptancedegree that is a prediction value of a degree to which an active userthat is a target user accepts a combination including at least one ofthe content, the user, and the feedback, based on the collected metafeedback.

(13)

A program for causing a computer to execute processes including:

collecting meta feedback that is feedback on a combination including acontent, a user, and feedback on the content of the user; and

obtaining a predicted acceptance degree that is a prediction value of adegree to which an active user that is a target user accepts acombination including at least one of the content, the user, and thefeedback, based on the collected meta feedback.

(14)

An information processing device including:

a collection unit configured to collect meta feedback that is feedbackon a combination including a content and feedback on the content; and

a prediction unit configured to obtain a predicted acceptance degreethat is a prediction value of a degree to which an active user that is atarget user accepts a combination including at least one of the contentand the feedback, based on the collected meta feedback.

(15)

The information processing device according to (14), further including:

a selection unit configured to select a combination including a contentto be presented to the active user and the feedback given on thecontent, based on the predicted acceptance degree; and

a presentation control unit configured to control the content includedin the selected combination and the feedback included in the selectedcombination to be presented to the active user.

(16)

The information processing device according to (14), further including:

a counting unit configured to count the predicted acceptance degrees ofa plurality of active users for each feedback;

a selection unit configured to select a combination including thecontent to be presented and the feedback, based on the counted result ofthe predicted acceptance degrees; and

a presentation control unit configured to control the content includedin the selected combination and the feedback included in the selectedcombination to be presented.

(17)

The information processing device according to (14), wherein theprediction unit includes:

an acceptance model generation unit configured to generate an acceptancemodel for obtaining the predicted acceptance degree, based on thecollected meta feedback; and

an acceptance prediction unit configured to obtain the predictedacceptance degree based on the acceptance model.

(18)

An information processing method including:

collecting, by an information processing device, meta feedback that isfeedback on a combination including a content and feedback on thecontent; and

obtaining, by the information processing device, a predicted acceptancedegree that is a prediction value of a degree to which an active userthat is a target user accepts a combination including at least one ofthe content and the feedback, based on the collected meta feedback.

(19)

A program for causing a computer to execute processes including:

collecting meta feedback that is feedback on a combination including acontent and feedback on the content; and

obtaining a predicted acceptance degree that is a prediction value of adegree to which an active user that is a target user accepts acombination including at least one of the content and the feedback,based on the collected meta feedback.

What is claimed is:
 1. An information processing apparatus comprising:circuitry configured to collect feedback information on at least one ofa content, a first user, and a feedback for the content or for the firstuser, analyze the feedback information to determine a feature amount ofthe feedback information and a feature amount of the first user, andprovide, to a second user, another content based on the feature amountof the feedback information and the feature amount of the first user. 2.The information processing apparatus according to claim 1, wherein thecircuitry is further configured to perform an analysis to determine aninfluencer using the feature amount of the feedback information and thefeature amount of the first user.
 3. The information processingapparatus according to claim 2, wherein the influencer corresponds to aninfluential user from whom corresponding feedback information is readilyaccepted by the second user.
 4. The information processing apparatusaccording to claim 3, wherein the second user is an active user of theinformation processing apparatus.
 5. The information processingapparatus according to claim 1, wherein the feature amount of thefeedback information is associated with at least one feature elementextracted from the feedback information.
 6. The information processingapparatus according to claim 1, wherein the second user is an activeuser of the information processing apparatus.
 7. An informationprocessing method comprising: collecting feedback information on atleast one of a content, a first user, and a feedback for the content orfor the first user, analyzing the feedback information to determine afeature amount of the feedback information and a feature amount of thefirst user, and providing, to a second user, another content based onthe feature amount of the feedback information and the feature amount ofthe first user.
 8. The information processing method according to claim7, further comprising: performing an analysis to determine an influencerusing the feature amount of the feedback information and the featureamount of the first user.
 9. The information processing method accordingto claim 8, wherein the influencer corresponds to an influential userfrom whom corresponding feedback information is readily accepted by thesecond user.
 10. The information processing method according to claim 9,wherein the second user is an active user of the information processingapparatus.
 11. The information processing method according to claim 7,wherein the feature amount of the feedback information is associatedwith at least one feature element extracted from the feedbackinformation.
 12. The information processing method according to claim 7,wherein the second user is an active user of the information processingapparatus.
 13. A non-transitory computer-readable medium having embodiedthereon a program, which when executed by a computer causes the computerto execute a method, the method comprising: collecting feedbackinformation on at least one of a content, a first user, and a feedbackfor the content or for the first user, analyzing the feedbackinformation to determine a feature amount of the feedback informationand a feature amount of the first user, and providing, to a second user,another content based on the feature amount of the feedback informationand the feature amount of the first user.